Implementing Machine Learning Driven Optimization Models for Hybrid Multi-Cloud Environments and Automated Resource Management
Abstract
The rapid adoption of hybrid multi-cloud environments has transformed modern computing infrastructures by enabling organizations to combine private cloud resources with multiple public cloud platforms to achieve scalability, flexibility, and cost efficiency. However, managing these complex environments has become increasingly challenging due to dynamic workloads, heterogeneous architectures, unpredictable resource demands, and operational complexity. Machine learning (ML)-driven optimization models provide intelligent approaches for automated resource management by analyzing large-scale operational data, predicting future workloads, and making adaptive decisions regarding resource allocation, scheduling, and performance optimization. This research explores the implementation of machine learning techniques for improving resource utilization, reducing operational costs, enhancing system reliability, and supporting autonomous decision-making in hybrid multi-cloud infrastructures. The study examines predictive analytics, reinforcement learning, deep learning, and optimization algorithms as mechanisms for automated cloud resource management. A research methodology based on experimental evaluation, simulation-based analysis, and performance comparison is proposed to assess the effectiveness of ML-based optimization frameworks. Key performance indicators such as resource utilization, response time, energy efficiency, scalability, and cost reduction are considered for evaluation. The research highlights how intelligent automation can address limitations of traditional cloud management approaches and establish more adaptive, efficient, and resilient hybrid multi-cloud ecosystems capable of responding to continuously changing computational requirements
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 11 No. 3 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
21064-21072 |
Published |
June 11, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Cynthiya Mohan (%2026). Implementing Machine Learning Driven Optimization Models for Hybrid Multi-Cloud Environments and Automated Resource Management. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 3 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21064-21072. https://doi.org/10.15662/ijetemr.2026.1103008 |
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